We propose Mobile Audio Streaming Networks (MASnet) for efficient low-latency
speech enhancement, which is particularly suitable for mobile devices and other
applications where computational capacity is a limitation. MASnet processes
linear-scale spectrograms, transforming successive noisy frames into
complex-valued ratio masks which are then applied to the respective noisy
frames. MASnet can operate in a low-latency incremental inference mode which
matches the complexity of layer-by-layer batch mode. Compared to a similar
fully-convolutional architecture, MASnet incorporates depthwise and pointwise
convolutions for a large reduction in fused multiply-accumulate operations per
second (FMA/s), at the cost of some reduction in SNR.

提出了适用于移动设备和计算能力受限应用的移动音频流网络 (MASnet)，用于有效的低延迟语音增强。MASnet 将连续的嘈杂帧转换为复值比率掩码，然后将其应用于相应的嘈杂帧。与类似的全卷积架构相比，MASnet 采用了深度和点卷积，大大减少了每秒融合乘累加运算次数 (FMA/s)，但牺牲了一些信噪比。